scholarly journals Heterogeneous DNA Methylation Patterns in the GSTP1 Promoter Lead to Discordant Results between Assay Technologies and Impede Its Implementation as Epigenetic Biomarkers in Breast Cancer

Genes ◽  
2015 ◽  
Vol 6 (3) ◽  
pp. 878-900 ◽  
Author(s):  
Grethe Alnaes ◽  
Jo Ronneberg ◽  
Vessela Kristensen ◽  
Jörg Tost
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Epigenetic Biomarkers ◽  
Methylation Patterns

scholarly journals DNA Methylation Patterns Can Estimate Nonequivalent Outcomes of Breast Cancer with the Same Receptor Subtypes

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0142279 ◽  
Author(s):  
Min Zhang ◽  
Shaojun Zhang ◽  
Yanhua Wen ◽  
Yihan Wang ◽  
Yanjun Wei ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Receptor Subtypes ◽  
Methylation Patterns

scholarly journals Co-enzyme Q10, riboflavin and niacin supplementation on alteration of DNA repair enzyme and DNA methylation in breast cancer patients undergoing tamoxifen therapy

2008 ◽  
Vol 100 (6) ◽  
pp. 1179-1182 ◽  
Author(s):  
Vummidi Giridhar Premkumar ◽  
Srinivasan Yuvaraj ◽  
Palanivel Shanthi ◽  
Panchanatham Sachdanandam
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Dna Repair ◽  
Cancer Patients ◽  
Good Prognosis ◽  
Breast Cancer Patients ◽  
Repair Enzymes ◽  
Methylation Patterns ◽  
To Receive ◽  
Supplement Therapy

In the present study, eighty-four breast cancer patients were randomized to receive a daily supplement of 100 mg co-enzyme Q10, 10 mg riboflavin and 50 mg niacin (CoRN), one dosage per d along with 10 mg tamoxifen twice per d. A significant increase in poly(ADP-ribose) polymerase levels and disappearance of RASSF1A DNA methylation patterns were found in patients treated with supplement therapy along with tamoxifen compared to untreated breast cancer patients and tamoxifen alone-treated patients. An increase in DNA repair enzymes and disappearance of DNA methylation patterns attributes to reduction in tumour burden and may suggest good prognosis and efficacy of the treatment.

scholarly journals Identifying Potential Drivers of Differential DNA Methylation Patterns in Breast Cancer Cells

2015 ◽  
Vol 29 (S1) ◽  
Author(s):  
Alexis Fennoy ◽  
Jerry Fong ◽  
Jared Andrews ◽  
Andrew Gasparrini ◽  
John Edwards
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Methylation Patterns

scholarly journals An analytical pipeline for DNA Methylation Array Biomarker Studies

2021 ◽  
Author(s):  
Jennifer Lu ◽  
Darren Korbie ◽  
Matt Trau
Keyword(s):  
Dna Methylation ◽  
Feature Selection ◽  
Differential Methylation ◽  
The Cancer Genome Atlas ◽  
Methylation Array ◽  
Epigenetic Biomarkers ◽  
Cancer Genome Atlas ◽  
Dna Methylation Array ◽  
Methylation Patterns ◽  
Selection Of

DNA methylation is one of the most commonly studied epigenetic biomarkers, due to its role in disease and development. The Illumina Infinium methylation arrays still remains the most common method to interrogate methylation across the human genome, due to its capabilities of screening over 480, 000 loci simultaneously. As such, initiatives such as The Cancer Genome Atlas (TCGA) have utilized this technology to examine the methylation profile of over 20,000 cancer samples. There is a growing body of methods for pre-processing, normalisation and analysis of array-based DNA methylation data. However, the shape and sampling distribution of probe-wise methylation that could influence the way data should be examined was rarely discussed. Therefore, this article introduces a pipeline that predicts the shape and distribution of normalised methylation patterns prior to selection of the most optimal inferential statistics screen for differential methylation. Additionally, we put forward an alternative pipeline, which employed feature selection, and demonstrate its ability to select for biomarkers with outstanding differences in methylation, which does not require the predetermination of the shape or distribution of the data of interest. Availability: The Distribution test and the feature selection pipelines are available for download at: https://github.com/uqjlu8/DistributionTest Keywords: DNA methylation, Biomarkers, Cancers, Data Distribution, TCGA, 450K

scholarly journals Analysis of Global and Local DNA Methylation Patterns in Blood Samples of Patients With Autism Spectrum Disorder

2021 ◽  
Vol 9 ◽  
Author(s):  
María Victoria García-Ortiz ◽  
María José de la Torre-Aguilar ◽  
Teresa Morales-Ruiz ◽  
Antonio Gómez-Fernández ◽  
Katherine Flores-Rojas ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Dna Methylation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Control Group ◽  
Healthy Children ◽  
Blood Samples ◽  
Epigenetic Biomarkers ◽  
Increased Risk ◽  
Methylation Patterns

The goal of this investigation was to determine whether there are alterations in DNA methylation patterns in children with autism spectrum disorder (ASD).Material and Methods: Controlled prospective observational case-control study. Within the ASD group, children were sub-classified based on the presence (AMR subgroup) or absence (ANMR subgroup) of neurodevelopmental regression during the first 2 years of life. We analyzed the global levels of DNA methylation, reflected in LINE-1, and the local DNA methylation pattern in two candidate genes, Neural Cell Adhesion Molecule (NCAM1) and Nerve Growth Factor (NGF) that, according to our previous studies, might be associated to an increased risk for ASD. For this purpose, we utilized blood samples from pediatric patients with ASD (n = 53) and their corresponding controls (n = 45).Results: We observed a slight decrease in methylation levels of LINE-1 in the ASD group, compared to the control group. One of the CpG in LINE-1 (GenBank accession no.X58075, nucleotide position 329) was the main responsible for such reduction, highly significant in the ASD subgroup of children with AMR (p < 0.05). Furthermore, we detected higher NCAM1 methylation levels in ASD children, compared to healthy children (p < 0.001). The data, moreover, showed higher NGF methylation levels in the AMR subgroup, compared to the control group and the ANMR subgroup. These results are consistent with our prior study, in which lower plasma levels of NCAM1 and higher levels of NGF were found in the ANMR subgroup, compared to the subgroup that comprised neurotypically developing children.Conclusions: We have provided new clues about the epigenetic changes that occur in ASD, and suggest two potential epigenetic biomarkers that would facilitate the diagnosis of the disorder. We similarly present with evidence of a clear differentiation in DNA methylation between the ASD subgroups, with or without mental regression.

scholarly journals DNA Methylation Heterogeneity Patterns in Breast Cancer Cell Lines

2016 ◽  
Vol 15s4 ◽  
pp. CIN.S40300
Author(s):  
Sunny Tian ◽  
Karina Bertelsmann ◽  
Linda Yu ◽  
Shuying Sun
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Hub Genes ◽  
Methylation State ◽  
Methylation Patterns

Heterogeneous DNA methylation patterns are linked to tumor growth. In order to study DNA methylation heterogeneity patterns for breast cancer cell lines, we comparatively study four metrics: variance, I2 statistic, entropy, and methylation state. Using the categorical metric methylation state, we select the two most heterogeneous states to identify genes that directly affect tumor suppressor genes and high- or moderate-risk breast cancer genes. Utilizing the Gene Set Enrichment Analysis software and the ConsensusPath Database visualization tool, we generate integrated gene networks to study biological relations of heterogeneous genes. This analysis has allowed us to contribute 19 potential breast cancer biomarker genes to cancer databases by locating “hub genes” – heterogeneous genes of significant biological interactions, selected from numerous cancer modules. We have discovered a considerable relationship between these hub genes and heterogeneously methylated oncogenes. Our results have many implications for further heterogeneity analyses of methylation patterns and early detection of breast cancer susceptibility.

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